Information, Volume 14, Issue 1
2023 January - 56 articles
Cover Story: Contemporary malware detection techniques are no longer considered as sufficient to detect modern mobile malware. To improve detection, machine learning (ML)-based algorithms have been brought to the foreground. However, applying ML techniques for predicting malware is a cumbersome process. In this context, the current work investigates the use of ML algorithms for mobile malware detection in a more holistic manner. Specifically, it explores the performance of nearly thirty different supervised and semi-supervised ML algorithms, including a DNN model. It conducts a comparative analysis in terms of prediction accuracy and other relevant key metrics, proceeds with hyperparameter tuning using the Optuna framework, and enables the SHAP framework to reveal the features that affect the prediction of malware. View this paper - Issues are regarded as officially published after their release is announced to the table of contents alert mailing list .
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